{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Render your design to Ansys\n",
    "\n",
    "This notebook describes methods to directly interact with the renderer. As a designer, you might prefer this degree of control. Alternatively, you can decide to only interact with the Analysis modules (described in the 4.* tutorial notebooks) which will take care of some of these operations for you. they also provide a level of abstraction for which you will not need to learn the separate renderers, but seemlesly utilize any of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisite\n",
    "You need to have Ansys installed locally (any version) - at the time of this notebook, Ansys is only supported in Windows.\n",
    "\n",
    "## 1. Create the design in Metal\n",
    "#### 1.1 Preload libraries and classes, then open Metal\n",
    "Let's start by importing all the key libraries and classes, and opening Metal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, MetalGUI\n",
    "from qiskit_metal import Dict, Headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lj': '10nH',\n",
       " 'Cj': 0,\n",
       " '_Rj': 0,\n",
       " 'max_mesh_length_jj': '7um',\n",
       " 'project_path': None,\n",
       " 'project_name': None,\n",
       " 'design_name': None,\n",
       " 'x_buffer_width_mm': 0.2,\n",
       " 'y_buffer_width_mm': 0.2,\n",
       " 'wb_threshold': '400um',\n",
       " 'wb_offset': '0um',\n",
       " 'wb_size': 5,\n",
       " 'plot_ansys_fields_options': {'name': 'NAME:Mag_E1',\n",
       "  'UserSpecifyName': '0',\n",
       "  'UserSpecifyFolder': '0',\n",
       "  'QuantityName': 'Mag_E',\n",
       "  'PlotFolder': 'E Field',\n",
       "  'StreamlinePlot': 'False',\n",
       "  'AdjacentSidePlot': 'False',\n",
       "  'FullModelPlot': 'False',\n",
       "  'IntrinsicVar': \"Phase='0deg'\",\n",
       "  'PlotGeomInfo_0': '1',\n",
       "  'PlotGeomInfo_1': 'Surface',\n",
       "  'PlotGeomInfo_2': 'FacesList',\n",
       "  'PlotGeomInfo_3': '1'}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.renderers.renderer_ansys.ansys_renderer import QAnsysRenderer\n",
    "QAnsysRenderer.default_options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 Prepare a design consisting of 4 qubits and 4 CPWs\n",
    "First we import the necessary components and change key global variables to the desired size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.variables['cpw_width'] = '15 um'\n",
    "design.variables['cpw_gap'] = '9 um'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create the 4-Qubit-4-CPWs design in one shot. Feel free to break the cell below into subcells if you would like to more closely follow the layout generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable overwrite, so that we can re-un this cell multiple times without incurring object name collisions\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "# We now define the same custom options for all the transmons.\n",
    "# We only list the options we intend to modify with respect to the default values\n",
    "options = Dict(\n",
    "    pad_width = '425 um', \n",
    "    pocket_height = '650um',\n",
    "    # Adding 3 connection_pads\n",
    "    connection_pads=Dict(\n",
    "        a = dict(loc_W=+1,loc_H=-1, pad_width='200um'),\n",
    "        b = dict(loc_W=-1,loc_H=+1, pad_height='30um'),\n",
    "        c = dict(loc_W=-1,loc_H=-1, pad_height='50um')\n",
    "    )\n",
    ")\n",
    "\n",
    "# Create the 4 transmons\n",
    "q1 = TransmonPocket(design, 'Q1', options = dict(\n",
    "    pos_x='+2.42251mm', pos_y='+0.0mm', **options))\n",
    "q2 = TransmonPocket(design, 'Q2', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='-0.95mm', orientation = '270', **options))\n",
    "q3 = TransmonPocket(design, 'Q3', options = dict(\n",
    "    pos_x='-2.42251mm', pos_y='+0.0mm', orientation = '180', **options))\n",
    "q4 = TransmonPocket(design, 'Q4', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='+0.95mm', orientation = '90', **options))\n",
    "\n",
    "# Now we do the same for the CPWs\n",
    "options = Dict(\n",
    "        lead=Dict(\n",
    "            start_straight='0.2mm',\n",
    "            end_straight='0.2mm'),\n",
    "        trace_gap='9um',\n",
    "        trace_width='15um')\n",
    "\n",
    "# We define a handy method to shorten the number of line of code necessary to define the 4 CPW\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str, asymmetry='0 um', flip=False, fillet='90um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        fillet=fillet,\n",
    "        hfss_wire_bonds = True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        total_length=length)\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "# Create the 4 CPWs\n",
    "asym = 140\n",
    "cpw1 = connect('cpw1', 'Q1', 'c', 'Q2', 'b', '5.6 mm', f'+{asym}um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'b', 'Q2', 'c', '5.7 mm', f'-{asym}um', flip=True)\n",
    "cpw3 = connect('cpw3', 'Q3', 'c', 'Q4', 'b', '5.6 mm', f'+{asym}um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'b', 'Q4', 'c', '5.7 mm', f'-{asym}um', flip=True)\n",
    "\n",
    "# Render the entire design to the Qiskit Metal GUI\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Render into Ansys HFSS\n",
    "The Ansys HFSS renderer was instantiated during the boot of Qiskit Metal. Let's create a short handle to refer to it in the rest of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss = design.renderers.hfss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Connect to or create an Ansys project\n",
    "To setup the project **manually**, follow these instructions:\n",
    "1. Launch `ANSYS Electronics Desktop yyyy Rx` (from your Windows Start menu).\n",
    "2. Create a new Ansys project by clicking on the `New` icon at the top left. (or open an existing project)\n",
    "\n",
    "Alternatively, you can **automatically** set up Ansys by executing the following cell. If Ansys is already open, executing this cell will connect this jupyter notebook session to the currently open windo, project, design and setup. you can modify that later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:08PM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 04:08PM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 04:08PM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 04:08PM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project22\n",
      "INFO 04:08PM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 04:08PM [connect]: \t Connected to project \"Project22\". No design detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourq_hfss.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 Setup an Ansys HFSS design\n",
    "You can either create a new design or select and use an old one.\n",
    "\n",
    "##### Creating a design\n",
    "\n",
    "To create a new design **manually**, go to the Ansys GUI and follow these instructions:\n",
    "1. Select the project from the leftmost menu in the Ansys GUI.\n",
    "2. Go into the menu `Project` and select `Insert HFSS Design`.\n",
    "3. Change the HFSS design to either eigenmode or modal by right-clicking on the HFSSdesign1 that just got created inside your project (left panel) and then selecting: `Solution Type...`.\n",
    "\n",
    "To create a new design **automatically**, execute one of the following two cells. The first will create a design with eigenmode solution type, the second one will create a design with drivenmodal solution type.\n",
    "\n",
    "NOTE: The design will be added to the project that was active when the command `fourq_hfss.start()` was executed.\n",
    "Note: If a design named `HFSSTransmonQubit` already exists in the project, a new design will be created, with the name suffixed with an incremental integer: `HFSSTransmonQubit1`, `HFSSTransmonQubit2`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:09PM [connect_design]: \tOpened active design\n",
      "\tDesign:    HFSSMetalEigenmode [Solution type: Eigenmode]\n",
      "WARNING 04:09PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 04:09PM [connect_setup]: \tCreating eigenmode default setup.\n",
      "INFO 04:09PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssEMSetup'>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'HFSSMetalEigenmode'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansys_design = fourq_hfss.new_ansys_design(\"HFSSMetalEigenmode\", 'eigenmode')\n",
    "ansys_design.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:09PM [connect_design]: \tOpened active design\n",
      "\tDesign:    HFSSMetalDrivenModal [Solution type: DrivenModal]\n",
      "WARNING 04:09PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 04:09PM [connect_setup]: \tCreating drivenmodal default setup.\n",
      "INFO 04:09PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'HFSSMetalDrivenModal'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansys_design = fourq_hfss.new_ansys_design(\"HFSSMetalDrivenModal\", 'drivenmodal')\n",
    "ansys_design.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Selecting a design previously created\n",
    "\n",
    "If you created a design with the above commands, it will be automatically selected and linked to this jupyter notebook session. Instead, if you want to use a design that had been created beforehand in the project, you can select it by following one of following three approaches:\n",
    "* manually activating the design from the Ansys GUI, **before** you run the `fourq_hfss.start()` command. In the Ansys GUI, you will find the list of designs in the leftmost panel, and you can activate them with a double click.\n",
    "* using method `fourq_hfss.connect_ansys(*with parameters*)`, specifying which design to connect to.\n",
    "* using method `activate_ansys_design('name')`.\n",
    "\n",
    "The next two cells exemplify how to use the last method. We will switch between the two designs that we previously created.\n",
    "\n",
    "NOTE: this method will also create a design with the given name if the design name is not found, to be able to create a new design it will need to be given the solution type, for example: `fourq_hfss.activate_ansys_design(\"newEigen\", 'eigenmode')`. Failure to specify the type will result in an error message asking to specify the the `solution_type` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:09PM [connect_design]: \tOpened active design\n",
      "\tDesign:    HFSSMetalEigenmode [Solution type: Eigenmode]\n"
     ]
    }
   ],
   "source": [
    "fourq_hfss.activate_ansys_design(\"HFSSMetalEigenmode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:09PM [connect_design]: \tOpened active design\n",
      "\tDesign:    HFSSMetalDrivenModal [Solution type: DrivenModal]\n"
     ]
    }
   ],
   "source": [
    "fourq_hfss.activate_ansys_design(\"HFSSMetalDrivenModal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Render some component from the Metal design\n",
    "\n",
    "Find below several rendering examples. You can choose to only execute one of them if you are just browsing this notebook.\n",
    "\n",
    "Notice how we explicitly clear the design before re-rendering. Indeed `render_design()` only adds shapes to the Ansys design. Re-rendering the same shapes will cause violations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.render_design()  # entire Metal design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.clean_active_design()\n",
    "fourq_hfss.render_design(['Q1'], [('Q1', 'b'), ('Q1', 'c')])  # single qubit with 2 endcaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.clean_active_design()\n",
    "fourq_hfss.render_design(['Q1', 'cpw1', 'Q2'], [('Q1', 'b'), ('Q2', 'c')])  # 2 qubits and 2 endcaps, one per qubit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Driven-Modal analysis, we can also add terminations. In the example below we render 1 qubit with 1 endcap and 1 port with a 70 Ohm termination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.clean_active_design()\n",
    "fourq_hfss.render_design(['Q2'], [('Q2', 'a')], [('Q2', 'b', '70')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous examples, rendering area dimensions is determined by the size of the selected geometries, with some buffer.\n",
    "\n",
    "For a more accurate control of the chip size, you need to disable the buffering as below. This will use `design._chips['main']['size']` to determine the rendering area dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "design._chips['main']['size']['size_y'] = '6mm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.clean_active_design()\n",
    "fourq_hfss.render_design([], [], box_plus_buffer=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also modify the chip size directly by updating `design._chips['main']['size']`. Example below.\n",
    "\n",
    "NOTE: we purposfully make the chip size smaller than the size of the geometry. This will cause a warning to show which will need to be fixed by the user intending to conduct a valid analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_hfss.clean_active_design()\n",
    "design._chips['main']['size']['size_x'] = '4mm'\n",
    "fourq_hfss.render_design([], [], box_plus_buffer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    Return back to original size, for the remainder of the notebook\n",
    "design._chips['main']['size']['size_x'] = '6mm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Create of select a setup\n",
    "This section is only needed if you plan to execute an analysis. This topic will be covered in the tutorial notebooks 4.x.\n",
    "\n",
    "To **select** a setup for the active design, you can use `activate_ansys_setup()`. If the setup exists, the QRenderer will reference the setup, otherwise, will make a new setup with the name give.  If no name given, the default name of \"Setup\" will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:11PM 12s WARNING [activate_ansys_setup]: The setup_name=SetupNEW was not in active design.  Setups in active design are: \n",
      "('Setup',).  A new setup will default values will be added to the design.  \n"
     ]
    }
   ],
   "source": [
    "fourq_hfss.activate_ansys_setup('SetupNEW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally** disconnect the Metal renderer from the Ansys session.\n",
    "\n",
    "NOTE: If you do not disconnect explicitly, you might not be able to close the Ansys GUI later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourq_hfss.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Render into Ansys Q3D\n",
    "The Ansys Q3D renderer was instantiated during the boot of Qiskit Metal. Let's create a short handle to refer to it in the rest of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_q3d = design.renderers.q3d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 Connect to or create an Ansys project\n",
    "See section 2.1 in this notebook for additional details and options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:11PM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 04:11PM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 04:11PM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 04:11PM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project22\n",
      "INFO 04:11PM [connect_design]: \tOpened active design\n",
      "\tDesign:    HFSSMetalDrivenModal [Solution type: DrivenModal]\n",
      "INFO 04:11PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.HfssDMSetup'>)\n",
      "INFO 04:11PM [connect]: \tConnected to project \"Project22\" and design \"HFSSMetalDrivenModal\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourq_q3d.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Setup an Ansys Q3D design\n",
    "You can either create a new design or select and use an old one.\n",
    "\n",
    "##### Creating a design\n",
    "\n",
    "To create a new design **manually**, go to the Ansys GUI and follow these instructions:\n",
    "1. Select the project from the leftmost menu in the Ansys GUI.\n",
    "2. Go into the menu `Project` and select `Insert Q3D Extractor Design`.\n",
    "\n",
    "To create a new design **automatically**, execute the following cell.\n",
    "\n",
    "NOTE: If a design named `Q3dMetalDesign` already exists in the project, a new design will be created, with the name suffixed with an incremental integer: `Q3dMetalDesign1`, `Q3dMetalDesign2`, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:11PM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q3dMetalDesign [Solution type: Q3D]\n",
      "WARNING 04:11PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 04:11PM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 04:11PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Q3dMetalDesign'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansys_design = fourq_q3d.new_ansys_design(\"Q3dMetalDesign\", 'capacitive')\n",
    "ansys_design.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Selecting a design previously created\n",
    "\n",
    "If you created a design with the above commands, it will be automatically selected and linked to this jupyter notebook session. Instead, if you want to use a design that had been created beforehand in the project, you can select it by following one of following three approaches:\n",
    "* manually activating the design from the Ansys GUI, **before** you run the `fourq_hfss.start()` command. In the Ansys GUI, you will find the list of designs in the leftmost panel, and you can activate them with a double click.\n",
    "* using method `fourq_q3d.connect_ansys(*with parameters*)`, specifying which design to connect to.\n",
    "* using method `fourq_q3d.activate_ansys_design('name')`.\n",
    "\n",
    "The next two cells exemplify how to use the last method. The first of the two cells below will create a new design because the name does not corrispond to any design in the project. The second cell will return to the previously created design.\n",
    "\n",
    "NOTE: this method will also create a design with the given name if the design name is not found, to be able to create a new design it will need to be given the solution type, for example: `fourq_hfss.activate_ansys_design(\"newEigen\", 'eigenmode')`. Failure to specify the type will result in an error message asking to specify the the `solution_type` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:11PM 14s WARNING [activate_ansys_design]: The design_name=Q3dMetalDesignNEW was not in active project.  Designs in active project are: \n",
      "['HFSSMetalEigenmode', 'Q3dMetalDesign', 'HFSSMetalDrivenModal'].  A new design will be added to the project.  \n",
      "INFO 04:11PM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q3dMetalDesignNEW [Solution type: Q3D]\n",
      "WARNING 04:11PM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 04:11PM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 04:11PM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    }
   ],
   "source": [
    "fourq_q3d.activate_ansys_design(\"Q3dMetalDesignNEW\", 'capacitive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 04:11PM [connect_design]: \tOpened active design\n",
      "\tDesign:    Q3dMetalDesign [Solution type: Q3D]\n"
     ]
    }
   ],
   "source": [
    "fourq_q3d.activate_ansys_design(\"Q3dMetalDesign\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 Render some component from the Metal design\n",
    "\n",
    "Find below several rendering examples. You can choose to only execute one of them if you are just browsing this notebook.\n",
    "\n",
    "Notice how we explicitly clear the design before re-rendering. Indeed `render_design()` only adds shapes to the Ansys design. Re-rendering the same shapes will cause violations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fourq_q3d.render_design()  # entire Metal design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_q3d.clean_active_design()\n",
    "fourq_q3d.render_design(['Q1'], [('Q1', 'b'), ('Q1', 'c')])  # single qubit with 2 endcaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_q3d.clean_active_design()\n",
    "fourq_q3d.render_design(['Q1', 'cpw1', 'Q2'], [('Q1', 'b'), ('Q2', 'c')])  # 2 qubits and 2 endcaps, one per qubit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous examples, rendering area dimensions is determined by the size of the selected geometries, with some padding.\n",
    "\n",
    "For a more accurate control of the chip size, you need to disable the buffering as below. This will use `design._chips['main']['size']` to determine the rendering area dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_q3d.clean_active_design()\n",
    "fourq_q3d.render_design([], [], box_plus_buffer=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also modify the chip size directly by updating `design._chips['main']['size']`. Example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourq_q3d.clean_active_design()\n",
    "design._chips['main']['size']['size_y'] = '4mm'\n",
    "fourq_q3d.render_design([], [], box_plus_buffer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    Return back to original size, for the remainder of the notebook\n",
    "design._chips['main']['size']['size_y'] = '6mm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 Create of select a setup\n",
    "This section is only needed if you plan to execute an analysis. This topic will be covered in the tutorial notebooks 4.x.\n",
    "\n",
    "To **select** a setup for the active design, you can use `activate_ansys_setup()`. If the setup exists, the QRenderer will reference the setup, otherwise, will make a new setup with the name give.  If no name given, the default name of \"Setup\" will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "04:13PM 53s WARNING [activate_ansys_setup]: The setup_name=SetupNEW was not in active design.  Setups in active design are: \n",
      "('Setup',).  A new setup will default values will be added to the design.  \n"
     ]
    }
   ],
   "source": [
    "fourq_q3d.activate_ansys_setup(\"SetupNEW\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finally** disconnect the Metal renderer from the Ansys session.\n",
    "\n",
    "NOTE: If you do not disconnect explicitly, you might not be able to close the Ansys GUI later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourq_q3d.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to close the Metal GUI, uncomment the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui.main_window.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References - Miscellaneous pyEPR/Ansys commands\n",
    "The following commands are for reference only to better understand how the backend code works. They're not meant to be run directly in this notebook as part of the tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import pyEPR as epr\n",
    "\n",
    "Connect to Ansys directly from notebook:\n",
    "\n",
    "pinfo = epr.ProjectInfo(project_path = None, \n",
    "                        project_name = None,\n",
    "                        design_name  = None)\n",
    "modeler = pinfo.design.modeler\n",
    "\n",
    "Access methods within HfssDesign class in pyEPR:\n",
    "\n",
    "epr.ansys.HfssDesign.create_dm_setup\n",
    "epr.ansys.HfssDesign.create_q3d_setup\n",
    "\n",
    "Get project and design names:\n",
    "\n",
    "pinfo.project_name\n",
    "design._design.GetName()\n",
    "\n",
    "Filter qgeometry table:\n",
    "\n",
    "full_table = design.qgeometry.tables['poly']\n",
    "mask = full_table['subtract'] == False\n",
    "table = full_table[mask]\n",
    "\n",
    "Draw centered rectangles:\n",
    "\n",
    "bigsquare = modeler.draw_rect_center([0, 0, 0], x_size=8, y_size=8, name='bigsquare')\n",
    "topright = modeler.draw_rect_center([2, 2, 0], x_size=2, y_size=2, name='topright')\n",
    "\n",
    "Subtracting shapes:\n",
    "\n",
    "modeler.subtract('bigsquare', ['topright'])\n",
    "\n",
    "Draw centered box:\n",
    "\n",
    "modeler.draw_box_center([0, 0, 0], [1, 2, 3])\n",
    "\n",
    "Draw closed polygon:\n",
    "\n",
    "trianglepts = [[-1, 5, 0], [1, 5, 0], [0, 7, 0]]\n",
    "modeler.draw_polyline(trianglepts, closed=True)\n",
    "\n",
    "Draw polyline:\n",
    "\n",
    "smallpts = [[2.85, 0, 0], [3.15, 0, 0]]\n",
    "modeler.draw_polyline(smallpts, closed=False)\n",
    "\n",
    "Sweep one polyline with another:\n",
    "\n",
    "modeler._sweep_along_path('Polyline8', 'Polyline7')"
   ]
  }
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